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 <h1 id="example-of-spark-sedona-hdfs-with-slave-nodes-and-osm-vector-data-consults">Example of spark + sedona + hdfs with slave nodes and OSM vector data consults<a class="headerlink" href="#example-of-spark-sedona-hdfs-with-slave-nodes-and-osm-vector-data-consults" title="Permanent link">&para;</a></h1>
 <div class="highlight"><pre><span></span><code>from IPython.display import display, HTML
 from pyspark.sql import SparkSession
 from pyspark import StorageLevel
 import pandas as pd
 from pyspark.sql.types import StructType, StructField,StringType, LongType, IntegerType, DoubleType, ArrayType
 from pyspark.sql.functions import regexp_replace
 from sedona.register import SedonaRegistrator
 from sedona.utils import SedonaKryoRegistrator, KryoSerializer
 from pyspark.sql.functions import col, split, expr
 from pyspark.sql.functions import udf, lit
 from sedona.utils import SedonaKryoRegistrator, KryoSerializer
 from pyspark.sql.functions import col, split, expr
 from pyspark.sql.functions import udf, lit, flatten
 from pywebhdfs.webhdfs import PyWebHdfsClient
 from datetime import date
 from pyspark.sql.functions import monotonically_increasing_id
 import json
 </code></pre></div>
 <h3 id="registering-spark-session-adding-node-executor-configurations-and-sedona-registrator">Registering spark session, adding node executor configurations and sedona registrator<a class="headerlink" href="#registering-spark-session-adding-node-executor-configurations-and-sedona-registrator" title="Permanent link">&para;</a></h3>
 <div class="highlight"><pre><span></span><code>spark = SparkSession.\
     builder.\
     appName(&quot;Overpass-API&quot;).\
     enableHiveSupport().\
     master(&quot;local[*]&quot;).\
     master(&quot;spark://spark-master:7077&quot;).\
     config(&quot;spark.executor.memory&quot;, &quot;15G&quot;).\
     config(&quot;spark.driver.maxResultSize&quot;, &quot;135G&quot;).\
     config(&quot;spark.sql.shuffle.partitions&quot;, &quot;500&quot;).\
     config(&#39; spark.sql.adaptive.coalescePartitions.enabled&#39;, True).\
     config(&#39;spark.sql.adaptive.enabled&#39;, True).\
     config(&#39;spark.sql.adaptive.coalescePartitions.initialPartitionNum&#39;, 125).\
     config(&quot;spark.sql.execution.arrow.pyspark.enabled&quot;, True).\
     config(&quot;spark.sql.execution.arrow.fallback.enabled&quot;, True).\
     config(&#39;spark.kryoserializer.buffer.max&#39;, 2047).\
     config(&quot;spark.serializer&quot;, KryoSerializer.getName).\
     config(&quot;spark.kryo.registrator&quot;, SedonaKryoRegistrator.getName).\
     config(&quot;spark.jars.packages&quot;, &quot;org.apache.sedona:sedona-spark-shaded-3.0_2.12:1.4.0,org.datasyslab:geotools-wrapper:1.4.0-28.2&quot;) .\
     enableHiveSupport().\
     getOrCreate()

 SedonaRegistrator.registerAll(spark)
 sc = spark.sparkContext
 </code></pre></div>
 <h3 id="connecting-to-overpass-api-to-search-and-downloading-data-for-saving-into-hdfs">Connecting to Overpass API to search and downloading data for saving into HDFS<a class="headerlink" href="#connecting-to-overpass-api-to-search-and-downloading-data-for-saving-into-hdfs" title="Permanent link">&para;</a></h3>
 <div class="highlight"><pre><span></span><code>import requests
 import json

 overpass_url = &quot;http://overpass-api.de/api/interpreter&quot;
 overpass_query = &quot;&quot;&quot;
 [out:json];
 area[name = &quot;Foz do Iguaçu&quot;];
 way(area)[&quot;highway&quot;~&quot;&quot;];
 out geom;
 &gt;;
 out skel qt;
 &quot;&quot;&quot;

 response = requests.get(overpass_url,
                          params={&#39;data&#39;: overpass_query})
 data = response.json()
 hdfs = PyWebHdfsClient(host=&#39;179.106.229.159&#39;,port=&#39;50070&#39;, user_name=&#39;root&#39;)
 file_name = &quot;foz_roads_osm.json&quot;
 hdfs.delete_file_dir(file_name)
 hdfs.create_file(file_name, json.dumps(data))
 </code></pre></div>
 <h3 id="connecting-spark-sedona-with-saved-hdfs-file">Connecting spark sedona with saved hdfs file<a class="headerlink" href="#connecting-spark-sedona-with-saved-hdfs-file" title="Permanent link">&para;</a></h3>
 <div class="highlight"><pre><span></span><code>path = &quot;hdfs://776faf4d6a1e:8020/&quot;+file_name
 df = spark.read.json(path, multiLine = &quot;true&quot;)
 </code></pre></div>
 <h3 id="consulting-and-organizing-data-for-analysis">Consulting and organizing data for analysis<a class="headerlink" href="#consulting-and-organizing-data-for-analysis" title="Permanent link">&para;</a></h3>
 <div class="highlight"><pre><span></span><code>from pyspark.sql.functions import explode, arrays_zip

 df.createOrReplaceTempView(&quot;df&quot;)
 tb = spark.sql(&quot;select *, size(elements) total_nodes from df&quot;)
 tb.show(5)

 isolate_total_nodes = tb.select(&quot;total_nodes&quot;).toPandas()
 total_nodes = isolate_total_nodes[&quot;total_nodes&quot;].iloc[0]
 print(total_nodes)

 isolate_ids = tb.select(&quot;elements.id&quot;).toPandas()
 ids = pd.DataFrame(isolate_ids[&quot;id&quot;].iloc[0]).drop_duplicates()
 print(ids[0].iloc[1])

 formatted_df = tb\
 .withColumn(&quot;id&quot;, explode(&quot;elements.id&quot;))

 formatted_df.show(5)

 formatted_df = tb\
 .withColumn(&quot;new&quot;, arrays_zip(&quot;elements.id&quot;, &quot;elements.geometry&quot;, &quot;elements.nodes&quot;, &quot;elements.tags&quot;))\
 .withColumn(&quot;new&quot;, explode(&quot;new&quot;))

 formatted_df.show(5)

 # formatted_df.printSchema()

 formatted_df = formatted_df.select(&quot;new.0&quot;,&quot;new.1&quot;,&quot;new.2&quot;,&quot;new.3.maxspeed&quot;,&quot;new.3.incline&quot;,&quot;new.3.surface&quot;, &quot;new.3.name&quot;, &quot;total_nodes&quot;)
 formatted_df = formatted_df.withColumnRenamed(&quot;0&quot;,&quot;id&quot;).withColumnRenamed(&quot;1&quot;,&quot;geom&quot;).withColumnRenamed(&quot;2&quot;,&quot;nodes&quot;).withColumnRenamed(&quot;3&quot;,&quot;tags&quot;)
 formatted_df.createOrReplaceTempView(&quot;formatted_df&quot;)
 formatted_df.show(5)
 # TODO atualizar daqui para baixo para considerar a linha inteira na lógica
 points_tb = spark.sql(&quot;select geom, id from formatted_df where geom IS NOT NULL&quot;)
 points_tb = points_tb\
 .withColumn(&quot;new&quot;, arrays_zip(&quot;geom.lat&quot;, &quot;geom.lon&quot;))\
 .withColumn(&quot;new&quot;, explode(&quot;new&quot;))

 points_tb = points_tb.select(&quot;new.0&quot;,&quot;new.1&quot;, &quot;id&quot;)

 points_tb = points_tb.withColumnRenamed(&quot;0&quot;,&quot;lat&quot;).withColumnRenamed(&quot;1&quot;,&quot;lon&quot;)
 points_tb.printSchema()

 points_tb.createOrReplaceTempView(&quot;points_tb&quot;)

 points_tb.show(5)

 coordinates_tb = spark.sql(&quot;select (select collect_list(CONCAT(p1.lat,&#39;,&#39;,p1.lon)) from points_tb p1 where p1.id = p2.id group by p1.id) as coordinates, p2.id, p2.maxspeed, p2.incline, p2.surface, p2.name, p2.nodes, p2.total_nodes from formatted_df p2&quot;)
 coordinates_tb.createOrReplaceTempView(&quot;coordinates_tb&quot;)
 coordinates_tb.show(5)

 roads_tb = spark.sql(&quot;SELECT ST_LineStringFromText(REPLACE(REPLACE(CAST(coordinates as string),&#39;[&#39;,&#39;&#39;),&#39;]&#39;,&#39;&#39;), &#39;,&#39;) as geom, id, maxspeed, incline, surface, name, nodes, total_nodes FROM coordinates_tb WHERE coordinates IS NOT NULL&quot;)
 roads_tb.createOrReplaceTempView(&quot;roads_tb&quot;)
 roads_tb.show(5)
 </code></pre></div>

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